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We describe a statistical signature of chunks and an algorithm for finding chunks. While there is no formal definition of chunks, they may be reliably identified as configurations with low internal entropy or unpredictability and high entropy at their boundaries. We show that the log frequency of a chunk is a measure of its internal entropy. The… (More)

Estimating the parameters of stochastic context-free grammars (SCFGs) from data is an important, well-studied problem. Almost without exception, existing approaches make repeated passes over the training data. The memory requirements of such algorithms are ill-suited for embedded agents exposed to large amounts of training data over long periods of time. We… (More)

This paper describes an unsupervised algorithm for segmenting categorical time series into episodes. The Voting-Experts algorithm first collects statistics about the frequency and boundary entropy of ngrams, then passes a window over the series and has two " expert methods " decide where in the window boundaries should be drawn. The algorithm successfully… (More)

We propose a novel batch active learning method that leverages the availability of high-quality and efficient sequential active-learning policies by approximating their behavior when applied for k steps. Specifically, our algorithm uses Monte-Carlo simulation to estimate the distribution of unlabeled examples selected by a sequential policy over k steps.… (More)

We consider the problem of finding minimum reset sequences in synchronizing automata. The well-knowň Cern´y conjecture states that every n-state synchronizing automaton has a reset sequence with length at most (n − 1) 2. While this conjecture gives an upper bound on the length of every reset sequence , it does not directly address the problem of finding the… (More)

This paper describes an unsupervised olgorirhm f o r segmenting categorical time series inro episodes. The VOTING-EXPERTS algorithm first collects starisrics about the frequency and boundav entmpy of ngrams. then passes a window over rhe series and has two " expert methods " decide where in rhe window boundaries should be drawn. The algorirhm successfully… (More)

The Hierarchical Agent Control Architecture (HAC) is a general toolkit for specifying an agent's behavior. HAC supports action abstraction, resource management, sensor integration, and is well suited to controlling large numbers of agents in dynamic environments. It relies on three hierarchies: action, sensor, and context. The action hierarchy controls the… (More)

We introduce the Constrained Subtree Selection (CSS) problem as a model for the optimal design of websites. Given a hierarchy of topics represented as a DAG G and a probability distribution over the topics, we select a subtree of the transitive closure of G which minimizes the expected path cost. We define path cost as the sum of the page costs along a path… (More)

We study the fundamental algorithmic rigidity problems for generic frameworks periodic with respect to a fixed lattice or a finite-order rotation in the plane. For fixed-lattice frameworks we give an O(n 2) algorithm for deciding generic rigidity and an O(n 3) algorithm for computing rigid components. If the order of rotation is part of the input, we give… (More)

We study constrained versions of the knapsack problem in which dependencies between items are given by a graph. In one version, an item can be selected only if one of its neighbours is also selected. In the other version, an item can be selected only when all its neighbours are also selected. These problems generalize and unify several problems including… (More)